A Fully Automated Post-Surgical Brain Tumor Segmentation Model for Radiation Treatment Planning and Longitudinal Tracking

• Published in: Cancers Vol. 15; no. 15; p. 3956
• Main Authors: Ramesh, Karthik K, Xu, Karen M, Trivedi, Anuradha G, Huang, Vicki, Sharghi, Vahid Khalilzad, Kleinberg, Lawrence R, Mellon, Eric A, Shu, Hui-Kuo G, Shim, Hyunsuk, Weinberg, Brent D
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 03.08.2023; MDPI
• Subjects: Accuracy; Adjuvant treatment; Algorithms; Automation; Biological products; Blood products; Brain cancer; Brain damage; Brain tumors; Cancer; Chemotherapy; Data imaging; Datasets; Deep learning; Edema; Glioblastoma; Internet software; Lesions; Longitudinal studies; Magnetic resonance imaging; Medical research; Medicine, Experimental; Morphology; Patients; Physicians; Radiation; Radiation therapy; Radiotherapy; Segmentation; Surgery; Tumors; deep learning; radiation treatment; longitudinal tracking; segmentation; glioblastoma
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers15153956

Glioblastoma (GBM) has a poor survival rate even with aggressive surgery, concomitant radiation therapy (RT), and adjuvant chemotherapy. Standard-of-care RT involves irradiating a lower dose to the hyperintense lesion in T2-weighted fluid-attenuated inversion recovery MRI (T2w/FLAIR) and a higher dose to the enhancing tumor on contrast-enhanced, T1-weighted MRI (CE-T1w). While there have been several attempts to segment pre-surgical brain tumors, there have been minimal efforts to segment post-surgical tumors, which are complicated by a resection cavity and postoperative blood products, and tools are needed to assist physicians in generating treatment contours and assessing treated patients on follow up. This report is one of the first to train and test multiple deep learning models for the purpose of post-surgical brain tumor segmentation for RT planning and longitudinal tracking. Post-surgical FLAIR and CE-T1w MRIs, as well as their corresponding RT targets (GTV1 and GTV2, respectively) from 225 GBM patients treated with standard RT were trained on multiple deep learning models including: Unet, ResUnet, Swin-Unet, 3D Unet, and Swin-UNETR. These models were tested on an independent dataset of 30 GBM patients with the Dice metric used to evaluate segmentation accuracy. Finally, the best-performing segmentation model was integrated into our longitudinal tracking web application to assign automated structured reporting scores using change in percent cutoffs of lesion volume. The 3D Unet was our best-performing model with mean Dice scores of 0.72 for GTV1 and 0.73 for GTV2 with a standard deviation of 0.17 for both in the test dataset. We have successfully developed a lightweight post-surgical segmentation model for RT planning and longitudinal tracking.